Unique ID: WB42
| Division: | EDU |
|---|---|
| Issue Date: | February 13th 2019 |
| Last modified: | February 22nd 2019 |
Using Big Data to Predict Student Achievement in Low-Income School Settings
Using Big Data to Predict Student Achievement in Low-Income School Settings
SDG: 04 - Quality Education08 - Decent Work & Economic Growth
Accurately predicting student performance early allows mitigating interventions to be effectively designed and applied. Prediction of student achievement is therefore highly valuable to policymakers. This proposal seeks to test whether existing Learning Outcome Predicting Artificial Neural Networks (LOPANNs) can perform with the same degrees of accuracy in lower-income settings as in higher-income settings. Using large data sets from Vietnam and Indonesia, it would determine LOPANNs could reproduce the accuracy they have achieved in the US, Belgium, and Argentina.
Project Sources
| Type Of Institution: | international organization |
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| Region: | East Asia & Pacific |
| Country Area: | Vietnam, Indonesia |
| Id Country Regional: | country |
SDG Indicators
| SDG Comments: | 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4a, 4b, 4c, 8.6 |
|---|---|
| SDG: | 04 - Quality Education08 - Decent Work & Economic Growth |
Other
| Income Level: | Lower-middle-income |
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| Timeframe To Produce Indicator: | NA |